Human‐modified landscapes driving the global primate extinction crisis
Bibliographic record
Abstract
Abstract The world's primates have been severely impacted in diverse and profound ways by anthropogenic pressures. Here, we evaluate the impact of various infrastructures and human‐modified landscapes on spatial patterns of primate species richness, at both global and regional scales. We overlaid the International Union for the Conservation of Nature (IUCN) range maps of 520 primate species and applied a global 100 km 2 grid. We used structural equation modeling and simultaneous autoregressive models to evaluate direct and indirect effects of six human‐altered landscapes variables (i.e., human footprint [HFP], croplands [CROP], road density [ROAD], pasture lands [PAST], protected areas [PAs], and Indigenous Peoples' lands [IPLs]) on global primate species richness, threatened and non‐threatened species, as well as on species with decreasing and non‐decreasing populations. Two‐thirds of all primate species are classified as threatened (i.e., Critically Endangered, Endangered, and Vulnerable), with ~86% experiencing population declines, and ~84% impacted by domestic or international trade. We found that the expansion of PAST, HFP, CROP, and road infrastructure had the most direct negative effects on primate richness. In contrast, forested habitat within IPLs and PAs was positively associated in safeguarding primate species diversity globally, with an even stronger effect at the regional level. Our results show that IPLs and PAs play a critical role in primate species conservation, helping to prevent their extinction; in contrast, HFP growth and expansion has a dramatically negative effect on primate species worldwide. Our findings support predictions that the continued negative impact of anthropogenic pressures on natural habitats may lead to a significant decline in global primate species richness, and likely, species extirpations. We advocate for stronger national and international policy frameworks promoting alternative/sustainable livelihoods and reducing persistent anthropogenic pressures to help mitigate the extinction risk of the world's primate species.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".